Extraction of Rules Dependencies for Optimization of Backward Inference Algorithm

This work presents the modification of backward inference algorithm for rule knowledge bases. Proposed algorithm extracts information of internal rules dependencies and performs only promising recursive calls. Optimization relies on reducing the number of rules searched for each run of inference and reducing the number of unnecessary recursive calls. We assume that the rule knowledge base itself contains enough information, which allow to improve the efficiency of the classic algorithms of the inference and we propose the decision units conception as tool for extracting and modeling such information. The first part of the work briefly presents backward inference algorithms in its classical version, next part of the work describes the decision units conception, then the utilization of decision units in optimization of inference algorithm is described and the modified versions of algorithm are presented. The preliminary evaluation of modified versions of algorithm finish presented work.